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Research Intern, Frontier Agents (Winter 2027)
Together AI · Research
- Where
- San Francisco
- Posted
- Sep 18
The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short.
As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined.
What you'd do
- Research and implement novel techniques in one or more of our focus areas
- Design and conduct rigorous experiments to validate hypotheses
- Document findings in scientific publications and blog posts
- Communicate the plans, progress, and results of projects to the broader team
- Training, developing, and evaluating frontier models, especially in the domain of agentic tasks and workflows
- Designing and curating datasets for frontier agents alignment and post-training
- Studying failure modes and developing safety paradigms for agentic behavior
- Research new recipes (for RL or test time scaling) for self-learning and long-context tasks completion
- Building agents that act on spoken input to carry out complex, multi-step tasks
- Developing ML infrastructure that can power agent operations at scale
What they want
- Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field
- Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech)
- Strong knowledge of Machine Learning and Deep Learning fundamentals
- Experience with deep learning frameworks (PyTorch, JAX, etc.)
- Understanding of how LLMs work
- Strong programming skills in Python
- Familiarity with Transformer architectures and recent developments in foundation models